{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8841c738",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA特征：\n",
      "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
      "0 -0.398580  0.370331 -0.139095 -0.001757  0.022082  0.049976 -0.005824   \n",
      "1 -0.178161  0.361036 -0.195391  0.044694  0.021599  0.077215 -0.042879   \n",
      "2 -0.139754  0.283417 -0.135300  0.093313 -0.009449  0.016007  0.030783   \n",
      "3 -0.214867  0.423153 -0.179919 -0.067448 -0.002963  0.011423  0.025622   \n",
      "4 -0.200329  0.366480 -0.154647 -0.018548 -0.003861 -0.003758 -0.007043   \n",
      "\n",
      "        PC8       PC9      PC10  \n",
      "0 -0.027778 -0.017358 -0.017483  \n",
      "1  0.016531  0.005683 -0.001665  \n",
      "2 -0.008683 -0.005999 -0.020968  \n",
      "3 -0.008075 -0.032538  0.001436  \n",
      "4  0.000725 -0.033286 -0.005318  \n",
      "小波变换特征：\n",
      "        WT0       WT1       WT2       WT3       WT4       WT5       WT6  \\\n",
      "0  4.026737  4.033205  4.024902  4.034408  4.033610  4.010168  3.608924   \n",
      "1  3.984635  3.987397  3.986359  3.986124  3.995847  3.951574  3.546918   \n",
      "2  3.984328  3.988511  3.980179  3.987523  3.990585  3.966659  3.547305   \n",
      "3  4.096879  4.094310  4.091987  4.093450  4.101039  4.075106  3.606451   \n",
      "4  4.045592  4.049011  4.039567  4.045856  4.055382  4.014773  3.578576   \n",
      "\n",
      "        WT7       WT8       WT9  ...     WT190     WT191     WT192     WT193  \\\n",
      "0  3.299820  2.857846  2.105338  ... -0.112414 -0.112258 -0.112796 -0.111672   \n",
      "1  3.261894  2.825341  2.082351  ... -0.127375 -0.126808 -0.127447 -0.126726   \n",
      "2  3.254125  2.816678  2.083337  ... -0.127565 -0.127515 -0.128088 -0.127470   \n",
      "3  3.288806  2.841853  2.093134  ... -0.124798 -0.124484 -0.124787 -0.124421   \n",
      "4  3.280629  2.840479  2.090938  ... -0.123215 -0.123538 -0.123981 -0.122944   \n",
      "\n",
      "      WT194     WT195     WT196     WT197     WT198     WT199  \n",
      "0 -0.110462 -0.108232 -0.105467 -0.106537 -0.106970 -0.107667  \n",
      "1 -0.125066 -0.123136 -0.120396 -0.121581 -0.122233 -0.121997  \n",
      "2 -0.125560 -0.123479 -0.120933 -0.122199 -0.122452 -0.122773  \n",
      "3 -0.122641 -0.120562 -0.118321 -0.118883 -0.119843 -0.119647  \n",
      "4 -0.121666 -0.119543 -0.116878 -0.117583 -0.117898 -0.117768  \n",
      "\n",
      "[5 rows x 200 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import pywt\n",
    "\n",
    "# 读取红外谱图数据\n",
    "file_path = 'F:\\\\研究\\\\番泻苷在线提取数据.xlsx'\n",
    "xls = pd.ExcelFile(file_path)\n",
    "ir_data = pd.read_excel(xls, '红外谱图', index_col='编号\\波数')\n",
    "\n",
    "# 使用PCA提取特征\n",
    "# 假设我们需要提取前10个主成分\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(ir_data)\n",
    "pca_features = pca.transform(ir_data)\n",
    "\n",
    "# 使用小波变换提取特征\n",
    "# 假设使用Daubechies 4小波基函数，并提取前3个近似系数\n",
    "wavelet = pywt.Wavelet('db4')\n",
    "coeffs = pywt.wavedec(np.array(ir_data), wavelet, level=3)\n",
    "wt_features = coeffs[0]\n",
    "\n",
    "# 将特征转换成DataFrame格式\n",
    "pca_features_df = pd.DataFrame(pca_features, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9', 'PC10'])\n",
    "\n",
    "# 小波变换特征提取\n",
    "# coeffs[0]是第一层的近似系数，其形状是(244, 200)\n",
    "# 所以需要调整DataFrame的列数\n",
    "wt_features_df = pd.DataFrame(coeffs[0], columns=['WT' + str(i) for i in range(200)])\n",
    "\n",
    "# 打印提取的特征\n",
    "print(\"PCA特征：\")\n",
    "print(pca_features_df.head())\n",
    "print(\"小波变换特征：\")\n",
    "print(wt_features_df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddd5fb31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        番泻苷A       番泻苷B\n",
      "0  24.933937  65.048774\n",
      "1  25.359890  62.287028\n",
      "2  27.850932  65.272927\n",
      "3  32.193367  75.099722\n",
      "4  33.313904  78.549335\n",
      "特征集样本数量： 403\n",
      "目标变量集样本数量： 403\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, MaxPooling1D, Flatten\n",
    "\n",
    "# 读取'番泻苷含量'工作表\n",
    "targets = pd.read_excel(xls, '番泻苷含量')\n",
    "# 选取番泻苷A和番泻苷B两列作为目标变量\n",
    "targets = targets[['番泻苷A', '番泻苷B']]\n",
    "# 查看目标变量的前几行\n",
    "print(targets.head())\n",
    "\n",
    "# 对特征集和目标变量集进行索引对齐\n",
    "pca_features_df, targets = pca_features_df.align(targets, join='inner', axis=0)\n",
    "\n",
    "# 再次检查对齐后的样本数量\n",
    "print(\"特征集样本数量：\", pca_features_df.shape[0])\n",
    "print(\"目标变量集样本数量：\", targets.shape[0])\n",
    "\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca_features_df, targets, test_size=0.2, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8822ce12",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n",
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\activations\\leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 154ms/step\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, LSTM, BatchNormalization, LeakyReLU, AveragePooling1D, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# 构建 ACLSTM 模型\n",
    "model_aclstm = Sequential()\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(LSTM(128, return_sequences=True))\n",
    "model_aclstm.add(LSTM(128))\n",
    "model_aclstm.add(Dense(2))\n",
    "model_aclstm.compile(optimizer=Adam(learning_rate=1e-3), loss='mean_squared_error')\n",
    "\n",
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0)\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "# 测试所需时间\n",
    "test_time = end_test_time - start_test_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b3ab429",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 198.12813591957092 seconds\n",
      "Testing time: 0.6873199939727783 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印时间\n",
    "print(f\"Training time: {train_time} seconds\")\n",
    "print(f\"Testing time: {test_time} seconds\")\n",
    "\n",
    "# 绘制训练和验证损失\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Train loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Training and Validation Loss Over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "faf9dd58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
      "ACLSTM MSE: 318.52139595872166\n",
      "ACLSTM R2: 0.9131765675971686\n",
      "ACLSTM MAE: 9.7917658989515\n",
      "ACLSTM RMSE: 17.847167729326735\n"
     ]
    }
   ],
   "source": [
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 计算回归指标\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))                  \n",
    "\n",
    "\n",
    "# 输出指标\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5a53e80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
      "ACLSTM MSE: 351.77362530559594\n",
      "ACLSTM R2: 0.9073599266159487\n",
      "ACLSTM MAE: 10.318213378393802\n",
      "ACLSTM RMSE: 18.75562916314982\n"
     ]
    }
   ],
   "source": [
    "# 测试 ACLSTM 模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 计算回归指标\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))                  \n",
    "\n",
    "\n",
    "# 输出指标\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)"
   ]
  }
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